Koh Yang Wei, Lee Hwee Kuan, Okabe Yutaka
Bioinformatics Institute, 30 Biopolis Street, no. 07-01, Matrix, Singapore 138671.
Department of Physics, Tokyo Metropolitan University, Hachioji, Tokyo 192-0397, Japan.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Nov;88(5):053302. doi: 10.1103/PhysRevE.88.053302. Epub 2013 Nov 4.
The density of states of continuous models is known to span many orders of magnitudes at different energies due to the small volume of phase space near the ground state. Consequently, the traditional Wang-Landau sampling which uses the same trial move for all energies faces difficulties sampling the low-entropic states. We developed an adaptive variant of the Wang-Landau algorithm that very effectively samples the density of states of continuous models across the entire energy range. By extending the acceptance ratio method of Bouzida, Kumar, and Swendsen such that the step size of the trial move and acceptance rate are adapted in an energy-dependent fashion, the random walker efficiently adapts its sampling according to the local phase space structure. The Wang-Landau modification factor is also made energy dependent in accordance with the step size, enhancing the accumulation of the density of states. Numerical simulations show that our proposed method performs much better than the traditional Wang-Landau sampling.
由于基态附近相空间体积较小，连续模型的态密度在不同能量下跨越多个数量级。因此，传统的王-兰道抽样方法对所有能量使用相同的试探移动，在对低熵态进行抽样时面临困难。我们开发了一种王-兰道算法的自适应变体，它能非常有效地在整个能量范围内对连续模型的态密度进行抽样。通过扩展布齐达、库马尔和斯文森的接受率方法，使试探移动的步长和接受率以能量依赖的方式进行调整，随机游走者能够根据局部相空间结构有效地调整其抽样。王-兰道修正因子也根据步长与能量相关，增强了态密度的累积。数值模拟表明，我们提出的方法比传统的王-兰道抽样表现要好得多。
